1. Field
The disclosed concept pertains generally to electric loads and, more particularly, to methods of identifying electric load types of electric loads. The disclosed concept also pertains to systems for identifying electric load types of electric loads.
2. Background Information
Electricity usage costs have become an increasing fraction of the total cost of ownership for commercial buildings. At the same time, miscellaneous electric loads (MELs) account for about 36% of electricity consumption of commercial buildings. Effective management of MELs could potentially improve energy savings of buildings up to about 10%. However, power consumption monitoring and energy management of MELs inside commercial buildings is often overlooked. In order to provide the MELs' energy consumption conditions by load type to a building automation system (BAS), and, consequently, to manage the MELs and reduce energy consumption inside commercial buildings, there is a need to identify the MELs.
Lam, H. Y. et al., “A novel method to construct taxonomy of electrical appliances based on load signatures,” IEEE Transactions on Consumer Electronics, vol. 53, no. 2, 2007, p. 653-60, discloses that a load signature is an electrical expression that a load device or appliance distinctly possesses. Load signatures can be applied to produce many useful services and products, such as, determining the energy usage of individual appliances, monitoring the health of critical equipment, monitoring power quality, and developing facility management tools. Load signatures of typical yet extensive loads are needed to be collected before applying them to different services and products. As there are an enormous number of electrical appliances, it is beneficial to classify the appliances for building a well-organized load signature database. A method to classify the loads employs a two-dimensional form of load signatures, voltage-current (V-I) trajectory, for characterizing typical household appliances. A hierarchical clustering method uses a hierarchical decision tree or dendrogram to show how objects are related to each other. Groups of the objects can be determined from the dendrogram, to classify appliances and construct the taxonomy of the appliances. The taxonomy based on V-I trajectory is compared to the taxonomies based on traditional power metrics and eigenvectors in prior studies.
In this taxonomy approach, only one set of load features is utilized, and the hierarchical structure of appliances, a dendrogram, is based on the selection of a distance value/threshold between the groups in each level, or the height of a cluster tree. In this approach, the selection of the distance/height will affect how the hierarchical tree is built.
It is known to capture the unique characteristics (e.g., without limitation, voltage; current; power of plugged loads at a power outlet or receptacle) of MELs.
The power usage monitoring of MELs by types in residential/commercial buildings provides an opportunity to effectively manage MELs and potentially improve energy savings of buildings. This needs an accurate and un-ambiguous identification of MELs that are plugged into power outlets.
To successfully identify MELs, the biggest challenge is to distinguish the loads with the most similarity, for example and without limitation, a DVD, a set-top box, and a PC monitor (e.g., without limitation, those using a standardized DC power supply, and current harmonic reduction techniques). It is believed that this difficulty has not been explicitly addressed and solved by known techniques.
Known proposals for detecting single-phase electric loads are based on voltage, current and/or power measurements, including, relative position in an active-reactive power plane (P-Q plane); variation in active and reactive power at different operating conditions; harmonic power contents and harmonic currents; steady-state two-dimensional voltage-current (V-I) trajectories; instantaneous power; instantaneous admittance; and power factor. However, it is believed that these proposals suffer from several serious disadvantages in their accuracy, robustness and applicability, including: (1) MELs with different voltage and current characteristics may be grouped together by the identifier if they consume approximately the same amount of active and reactive power; (2) MELs of same type may be grouped separately by the identifier if they have different power ratings; (3) steady-state operation is usually required for load detection, while many buildings' loads are dynamic in nature; and (4) MELs with similar current features cannot be distinguished, such as DVD players and set-top boxes, MELs with the same type of DC power supply, or other standardized power electronics interface circuits. These disadvantages together with the lack of an intelligent power outlet/strip capable of acquiring signals and processing algorithms have impeded the applications of these methods.
A self-organizing map (SOM) (also known as a self-organizing feature map (SOFM)) is a type of unsupervised artificial neural network that is trained using competitive learning to produce a relatively low-dimensional (typically two-dimensional), discretized representation of the input space of training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing relatively low-dimensional views of relatively high-dimensional data, akin to multidimensional scaling.
A self-organizing map consists of components called neurons (also known as nodes). Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space. The usual arrangement of nodes is a regular spacing in a hexagonal or rectangular grid. The SOM describes a mapping from a relatively higher dimensional data (or input) space to a relatively lower dimensional map space. The procedure for placing a vector from the data space onto the map is to first find the node with the closest weight vector to the vector taken from the data space. Once the closest node is located it is assigned the values from the vector taken from the data space, and this node is also called a “winner-neuron”. All the neurons within the winner's radius, defined by the neighborhood function, will update their weights as well. This method of training is called a “winner-takes-all” strategy.
A unified distance matrix (U-Matrix) is a common way to represent the information of the trained SOM. A U-Matrix value of a particular node is the average distance between the node and its closest neighbors. The number of the closest neighbor depends on its neighborhood topology. In a square grid for instance, the closest four or eight nodes (the Von Neumann neighborhood and Moore neighborhood, respectively) might be considered, or six nodes in a hexagonal grid. The distance between the adjacent neurons (or nodes) is presented with different colorings or gray scales. A dark coloring between the neurons corresponds to a relatively large distance and, thus, a gap between the vectors in the input data space. A light coloring between the neurons signifies that the vectors are relatively close to each other in the input data space. Light areas can be thought of as clusters and dark areas as cluster separators (or boundary regions). This way of representation is very helpful to find out clusters in the input data without having any a priori information about the clusters (e.g., in an unsupervised manner).
One of the biggest advantages of SOM is that it preserves statistical and topological information of the input data space, and classifies all training data into several groups by their inherent relationships, known as “clustering by nature”. As it thereby compresses information, while preserving the most important topological and metric relationships, the SOM can also be considered to produce some degree of abstractions.
SOM is an ideal self-clustering tool, but is not believed to be an effective classifier for the purpose of load identification.
There is room for improvement in methods of identifying electric load types of electric loads.
There is further room for improvement in systems for identifying electric load types of electric loads.